skysight-inc/HackerNews huggingface dataset:
It has been for a while, we ended up building our own test set to evaluate embedding models on our domain.

What we realized after doing this is that MTEB has always been a poor indicator, as embedding model performance varies wildly in-domain compared to out-of-domain. You'll get decent performance (lets say 70%) with most models, but eeking out gains over that is domain-dependent more than it is model-dependent.

Personally I recommend NV-Embed because it's easy to deploy and get the other performance measurements (e.g. speed) to be high spec. You can then simply enrich your data itself by e.g. using an LLM to create standardized artifacts that point back to the original text, kind of like an "embedding symlink."

Our observation has widely been that after standardizing data, the best-n models mostly perform the same.
Most of the country does not live in very high cost of living areas, so what you said isn't true in most places. And at some point, if your location is so bad that you just can't make it work financially, move. Yes, moving sucks for many reasons. But people are not powerless victims who just get stuck somewhere and can't do anything else, they have the ability to take themselves to another place where the opportunities are better.
Google Data Protocol
Is this study? https://pmc.ncbi.nlm.nih.gov/articles/PMC10275521/#brb33026-...

They give 347 nonbelievers and 287 believers a set of 32 locations. They must clasify them as (a) military bases, (b) hospitals, (c) schools (or education centers), or (d) cemeteries. The expected average is 8 but they get 8.31 and 10.09 respectively.

[I'm skiping a lot of confusing parts, like figure 4 and 5 that I can't understand what they mean and how are they related.]

Anyway, 8.31 for believers vs 10.09 for believers is interesting.

But ... from the article:

> A total of 347 participants who were nonbelievers in psychic experiences completed an RV experiment using targets based on location coordinates. A total of 287 participants reported beliefs in psychic experiences and completed another RV experiment using targets based on images of places.

These are two different tasks! It's impossible to know if the difference of the result is cused by nonbeliever/believer or cuased by coordinates/images.

As a technical opinion: This inmediately invalidates the whole study. I don't understand how this was even published.

As a personal opinion: It's obvious that the guys/gals with the photos would get better results than the guys/gals with only the coordinates. The CIA should build more spy planes and satelites.
Other notable constants and where they show up:

Euler–Mascheroni Constant (integrals and sums involving the harmonic series, Gamma functions)

Catalan’s Constant (certain trigonometric series, lattice Green’s function)

Feigenbaum Constants (logistic map, chaos in dynamical systems)

Khinchin’s Constant (partial quotients in simple continued fractions)

Glaisher–Kinkelin Constant (asymptotic expansions of the Barnes G-function, combinatorial limits and certain product expansions)

Ramanujan’s Constant (complex multiplication of elliptic curves)

Omega Constant (Omega times e to the power of Omega = 1, Lambert W function, x^x^x^... = 2)
Excellent advice. It’s also very important to know what any micro benchmarks you do have are really measuring. I’ve seen enough that actually measured the time to setup or parse something because they dominated and wasn’t cached correctly. Conversely I’ve seen cases where the JIT correctly optimised away almost everything because there was a check on the final value.

Oh, and if each op takes under a nanosecond than your benchmark is almost certainly completely broken.
Highly recommend this lecture by a working mathematician shared above (https://www.youtube.com/watch?v=vYCT7cw0ycw [54min]). It's very much grounded in history and experience, much more so than in speculation. I wrote a brief summary of some main points in that thread.

But specifically to your worry about humans just dotting i's and crossing t's, he predicts that exactly the opposite will happen. At the end he emphasizes that the ultimate goal of mathematics is more about human understanding than proving theorems.
> How much time do you need to learn lyrics of a song? How much time do you think a LLaMA 3.1 8B on a 2x3090 need?

Probably not the best example. How long does it take to input song lyrics into a file to have an operating system "learn" it?
HH is kinda the opposite of LIF on the abstraction spectrum.
> You can be in IT, discover a software product at work and decide you think the company is worth investing in.

And then you can be still be wrong because you didn't appreciate that the company is operating a low-trust environment/switching costs are high/they didn't have enough industry contacts/competitors outmarketed.
If that were the case, we wouldn't need GPL, we would just call it public domain. So why didn't we do that?
Are you running ml workloads or solving differential equations?

The two are rather different and one market is worth trillions, the other isn't.
Not very useful but thanks for the attempt :)
Perhaps it is becoming more clear that the current reproduction rates are unsustainable if they persist.

Anyway, I have a bunch of kids, and I think from a motivational perspective, yes, kids give you meaning for some years.

But they can also help you with something else due to the necessary self-sacrifices, which is getting a out of egocentric thoughts, such as what you're going to do with your life. Read a book on biology. You are a bunch of cells put in the world to cooperate to feed and reproduce before they self-terminate to leave space for the offspring. That's it. Stop fretting. :)

(Helping society and thereby the offspring of other bunches of cells is pretty noble too, by the way.)
Depends on where you live, in New England the salted winter roads tend to win and few cars last more than 10 years before there are holes in body panels.
Respectfully, I'm incredulous that you think trains need dramatically more infrastructure than aircraft. For that matter, I'd peg aircraft infrastructure as worse - the asphalt, the noise, the culling of all vegetation save scrubgrass, and all spread out over a massive area. At least a commuter train station can be (partially) covered and (IIRC from Germany) multi-layered

Notably, I say noise, and I suspect reasonable people will point out how not-quiet trains can be. I think the only argument I need to make for how much quieter they are than aircraft is pointing out on a map where the (commuter) train yards are (routinely city-center) vs airfields (typically the outskirts, far from where people live)

Aircraft require less infrastructure between points, but when you get there a rail yard doesn't require multi-kilometer runways (plural) and taxiways.

PS - My god, I hadn't realized how pro-train / anti-aircraft I was until I started on; even having previously worked in aviation, suggesting it was a damn good idea to leave. Aircraft may be an incredible feat of human engineering, but like the personal automobile, we as a society have taken it too far and used them in places that could do with fewer of them, IMO.
Would be nice if it was just a carrier for the compute modules, so as new ones are releases it could be upgraded
You describe the psychological motivations for gambling.
> because they have completely internalised the structure of the program and can think about it as a whole

This state is definitely how you can be most productive.

However, I've written multi-millions of lines of code and many different still active applications. It's gotten to the point where if someone asks me if an app has a particular feature, I have to open it to check even if I wrote the whole thing.

Not all projects I work on are one's where I still have the whole thing internalized.
Ask HN: How does programming affect your emotional state?
When I am coding for a long time, my emotional state tends to reflect the code. If it's not compiling when I think it should, I get frustrated. If everything is acting predictably, like I expect it to, I feel great.

After a day of programming, it takes me a while to get used to talking to a human being. During the day, my feelings reflect those of a computer - it is dry and unemotional, so I am too.

Does anyone else experience similar things? How do you deal?
You expect me to deal with all the -5% waste sources in one article?

Please explain me: what is the minimum share of total energy use that any activity should have before someone is allowed to dedicate any attention to it?
Very excited to see folks trying this out! Please get in contact with us (jruby team, either on social media or our matrix channel) if you have any troubles.
> for reasons that are never explained

They were explained in these two essays (enshittification):

https://pluralistic.net/2024/08/17/hack-the-planet/

https://www.wheresyoured.at/never-forgive-them/
"There are much fewer opportunities if they do not want to work on productizing large-scale language models, and these positions are disappearing quickly."

Yes. In 1900, the thing to be was an expert electrician. I had a friend with a PhD in bio who ended up managing a coffee shop because she'd picked the wrong branch of bio.

LLMs may be able to help with productizing LLMs, further reducing the need for people in that area.
There's a difference between free tiers and trials. A trial is fine, but the unlimited free tiers offered now are a part of a race to the bottom that make it so only the largest, most investor-backed companies and loss-leaders can effectively compete.
I use eclipse for most of my Serious Work. With most "clever" features turned off. The kind of code-completion that lets me browse a list of partial matches is very useful (e.g. was it foo.bar() or foo.baz()?). Other than that, I like to do my own thinking. I enjoy writing software, not maintaining software that was generated by some tool.

Do I remember every type and every field? Of course not. But my codebases are structured. There are naming conventions in place. I generally have an intuition of where to find things. And of course there are many ways of searching for stuff...
having worked on whole brain modeling the last 15 years and european infra for supporting this kinda research, this is a terrible buzzword salad. the pdf is on par with a typical masters project.
Sunday night games are also on NBC.
You can register for a free NLM account to access the value sets (VSAC). HL7 standards are approachable for small teams but due to the inherent complexity of healthcare it can take a while to get up to speed. The FHIR Fundamentals training course is a good option for those who are starting out.

https://www.hl7.org/training/fhir-fundamentals.cfm?ref=nav

It might seem tempting to avoid the complexity of FHIR and CQL by inventing your own simple schema or data formats for a narrow domain. But I guarantee that what you thought was simple will eventually grow and grow until you find that you've reinvented FHIR — badly. I've seen that happen over and over in other failed projects. Talk to the CodeX accelerator I linked above and they should be able to get you pointed in the right direction.
Anecdata: bought a used off-lease 2019 Model 3 in 2021 (NCA chemistry.) The battery had degraded a typical amount (<10%) as they do in the first few years. Since then, I have been doing a pretty good job of following the advice to basically keep the battery >20%, and <80% as much as possible, and only supercharge when absolutely necessary. It hasn’t really degraded much further at all, if the range estimate can be believed.

At the current rate, the suspension and other components seem more likely to fall apart (it was constructed in a hurry, in a tent after all) before the battery hits its EOL. Regardless, I hope to be able to reclaim the degraded cells to use in home energy storage if it is economically viable.
That's not what you said.

> It's in the Bill of Rights because it was so important. Not as a tool to restrict this right to just government infringements.

In the Bill of Rights, it's restricted to just government infringements.
Perhaps there is.

The trouble, then, would be that YouTube's implementation is either broken or nonexistent. (We can tell this because if it did exist and did work well, then there would not be complaints.)
Denmark boosts Greenland defence after Trump repeats desire for US control
I'd argue it's clear why it's hard for a neural net to figure out.

A ladder is a kind of a mechanical one-way sequence which is quite long to read out. This is easy for humans (it's a one-way street!) but hard for AI (the MCTS prefers to search wide rather than deep). It is easy to tell the neural net as one of its inputs eg "this ladder works" or "this ladder doesn't work" -- in fact that's exactly what KataGo does.

See the pictures for more details about ladders: https://senseis.xmp.net/?Ladder
We have all of the right tooling created to fetch jars from maven but no good tutorials on stitching it all together. Clearly that is step we should document better.

And for those following along, we keep the jbang configuration updated with every release so you know you're getting the most recent version.
I appreciated the humility in this paragraph in particular

> once the first generation of lucky PhD’s (including me!) who were there not out of career prospects but mostly out of luck (or unluck), we started to have a series of much more brilliant and purpose-driven PhD’s working on deep learning. because these people were extremely motivated and were selected not by luck but by their merits and zeal, they started to make a much faster and more visible progress. soon afterward, this progress started to show up as actual products.

I have to say, though, that AI and robotics are going through similar transitions as were highlighted in TFA. Robotics has been basically defined by self driving cars for a long time, and we're starting to see the closure of some very big programs. Already the exorbitant salaries are much lower based on what I've seen, and demand is flat lining. My hope is that the senior level engineers with good PhD backgrounds move out into the broader field and bring their experience and research zeal with them as a force multiplier. I expect the diaspora of talent to reinvigorate industry innovation in robotics.

So it will be with LLM-focused researchers in industry in the next phase after we pass peak hype. But the things those battle-scarred researchers will do for the adjacent fields that were not hype-cycled to death will probably be amazing.

Unless they succeed in replacing themselves with general AI. Then all bets are off.
Sure, it’s great from the consumer perspective in the short run. But how much does needing a free tier to attract customers raise the barrier to entry for the market? The only companies that can compete are those with existing infrastructure and revenue streams that can subsidize their losses. Even ZIRP couldn’t counteract that. There is a reason that predatory pricing is illegal.

Would strongly recommend Lina Khan’s “Amazon’s Antitrust Paradox”
Well he didn't have decades of propaganda but still roots for Israel... I didn't mention this subject there because I have better things to do with my time than ruin Christmas by talking politics with teenagers so I can't tell you where he got that from
So, since you cannot restore the victim, and past is past, you'll lecture victims about the cycle of violence and tell them to "just get over it"? And call it "justice"?
The problem with the battery pack serviceability are proprietary technology and lack of standardisation, making fixing and getting replacement parts hard. Also a lot of closed source software contributes to this. This could change in the future, though.
> No reason to force that on desktop users.

Of course it is, because of 'mobile first!' Anything else is just more work, and a cost center. So f... it!
I think for a lot of people it’s hard precisely for that reason - it’s a very social time of year for most which increases the feeling of loneliness for those who maybe don’t know a lot of people or don’t have family or friends they can visit for whatever reason.
Incorrect w&b can affect a lot of things. From longer ground roll during landing, to wrong brake settings, to worse handling of the aircraft and increased stall speeds. Under normal circumstances it should not be a problem due to huge safety margins, but if other things go wrong it starts adding up. One more hole in the swiss cheese model.
Interpretation 1 is more likely and made the dry mathematician more relatable/human which made the writing better imo.
> Should we not mimic our biology as closely as possible rather than trying to model how we __think__ it works (i.e. chain of thought, etc.).

Should we not mimic migrating birds’ biology as closely as possible instead of trying to engineer airplanes for transatlantic flight that are only very loosely inspired in the animals that actually fly?
Could someone clarify to me the different between dependent types and refinement types?
A point of anecdata: my 2010 Prius has nearly 200,000 miles on it and it's still going strong.

The Prius has a traditional lead acid battery for starting the engine that's been replaced a couple of times. The traction batteries (the ones that help push the car forward and are charged from regenerative braking) are the originals.
The SPY and QQQ charts zoomed out are starting to look like the biggest parabola of all time in markets. 1999 is just a blip on this monster.

Seeing all kinds of warning signs from family members who never traded or got into crypto until just this past 6 months.
"Hard to find a more American article than this." Just wait until they start taking pot-shots at them and then it will be "real" America: BOOM! "Look Ma! I done got one!"
Undefined behavior in C/C++ has a different meaning than you're using. If a compiler encounters a piece of code that does something whose behavior is undefined in the spec, it can theoretically emit code that does anything and still be compliant with the standards. This could include things like setting the device on fire and launching missiles, but more typically is something seemingly innocuous like ignoring that part of the code entirely.

An example I've seen in actual code:
You checked for null before dereferencing a variable, but there is one code path that bypasses the null check. The compiler knows that dereferencing a null pointer is undefined so it concludes that the pointer can never be null and removes the null checks from all of the code paths as an "optimization".

That's the C/C++ foot-gun of undefined behavior. It's very different from memory safety and correctness that you're conflating it with.
I mean HH is an elaboration of the LIF with the addition of several equations for the various ion channels, but yeah I see what you mean.
> At the current rate, the suspension and other components seem more likely to fall apart

They're trivial to replace when the time comes.
I'm a bit confused at what this is actually. Is it a modeling framework that you use to build and study a network? Is it a system that you use to help you analyze your neural recording datasets? Neuroscience is a big place, so I feel like maybe the article and technical paper is speaking to a different audience than me, a neuroimager.
Keywords vs. variables vs. functions <-- these all look the same.
Yeah that's something I disable in every tool I touch. Especially when I'm typing something like foo(), by the time any autocomplete inserts that closing parenthesis, I will have already typed it, and now I'm distracted.

I don't mind suggestions if they are unobtrusive, but anything that interrupts my typing is an immediate no-no.
I agree on all points except one. I'm unlikely to ever be found without one. Put another way, I don't, and don't plan on, knowing life without one (a dog or two).
Games of 2024
So are there an infinite amount of constants like this? In terms of pi, e and this number?

Just wondering, there are an infinite number of shapes I suppose? But does that mean there is an infinite amount of constants?
Also curious compared to Immich.

I've been following it closely since it got released and recieved funding from FUTO, and it's been a joy to use after I recently set it up in my homelab.
Video shows protesters outside Supreme Court in China's Chongqing
Tweakphp/tweakphp: Easily tweak your PHP code
> As with all LLM models and their subproducts, the only way to ensure good results is to test yourself, ideally with less subjective, real-world feedback metrics.

This is excellent advice. Sadly, very few people/organizations implement their own evaluation suites.

It doesn't make much sense to put data infrastructure in production without first evaluating its performance (IOPS, uptime, scalability, etc.) on internal workloads; it is no different for embedding models or models in general for that matter.
AI/ML techniques in medicine have been applied clinically since at least the 90s. Part of the reason you don't see them used ubiquitously is a combination of a) it hasn't worked all that well in many scenarios so far and b) medicine is by nature quite conservative, for a mix of good and not so good reasons.
The constitution also sets up free movement of labor among states. It works great too!

"The Law of Supply and Demand" was unknown to the Romans or various middle Eastern empires (just two examples), whose economies worked in entirely different ways from those that followed the mercantilist period in Europe, and became canonized by Smith et al.
No, in my case it was a theoretical work, but others have measured it e.g. https://www.nature.com/articles/s41586-020-1989-2
This is probably the best example of what it feels like to be someone dealing with cognitive dissonance of Elite Overproduction in AI.

https://en.m.wikipedia.org/wiki/Elite_overproduction

Since 2012 (Alexnet winning Imagenet with DL) “AI” has been dominated by corporations for one simple and obvious reason that Suttom has pointed out over and over: The group with the best data wins

That wasn’t always true. It used to be the case that the government had better data or academia had better data, but it’s not even close at this point to the point where both of those groups are nearly irrelevant in either applied AI or AI research.

I’ve been applying “AI” at every possible chance I have since 1998 (unreal engine + A* + knn …) and the field has foundationally changed three or four times since.

When I started, people looked at those of us that stated out loud that we wanted to work on AGI as totally insane.

Bengio spoke at the AGI 2014 conference, and at a lunch between myself, Ben G, Josha Bach and Bengio we all agreed DL was going to central to AGI - but it was unclear who would own the data. Someone argued that AGI would most likely come out of an open source project and my argument was there are no structured incentives to allow for the type of organization necessary at the open source level for that.

My position was that DL isn’t sufficient - we need a forward looking RL streaming reward system that is baked into the infrastructure between planning and actuation. I still think that’s true and why AGI (robots doing things instead of people at greater than human level) is probably only a decade away.

It’s pretty wild that stating out loud that your goal is to build superhuman artificial intelligence is still some kind crazy idea - even though it’s so obviously the trajectory we’re on.

As much as I dislike OpenAI and the rest of the corporate AI companies, I respect the fact that they’ve been very vocal about trying to build general intelligence and superhuman intelligence.
I think the phrase you're looking for is "automatic investing" or something, not DCA. DCA is usually framed explicitly as a counterpart to lump sum of an existing pile of money